The Kepler space telescope has scanned the Milky Way for years, watching for the telltale dip in brightness when a planet-sized object crosses in front of a star.

Its data set is a great playground for machine learning systems: noisy and voluminous, with subtle variations that could go undetected by simple statistical methods or human scrutiny. A convolutional neural network is just the trick to tease out new and interesting results from that morass.

As is so often the case, though, the AI has to follow a human example. It was trained on thousands of Kepler readings already labeled and verified as planet or non-planet, and learned the patterns that astronomers are interested in. This trained model was what ended up identifying Kepler-90i and Kepler-80g.

The researchers write that they hope releasing the source for the project will help make it more accurate and also perhaps allow the work to continue at a faster pace or be adapted to new data sets. You can read the documentation and fork the code yourself over at GitHub.